The human visual system is a capacity limited system in that it can only process a relatively small number of objects at any given time. This is true, despite the fact that there are many objects that may be visible at any given time. From the array of objects visible to a human, that human's visual system will only attend to, or processes, one (or very few) objects at any given time. When a human looks at an image or a scene, his visual system will shift attention (and mental processing) from one object to another.
There has been a substantial amount of research in the area of human visual attention. This research has generated numerous studies directed toward understanding the behavior of human visual attention, as well as many computational models of visual attention. These computation models (sometimes called visual attention models, eye-gaze prediction models, attention models, or saliency models) predict where, given visual stimuli (for example, a picture or a scene), a person will allocate their visual attention or gaze.
These models provide predictions about the objects or regions within the scene that will attract visual attention. Typical real world scenes, however, are often highly dynamic. The image projected to the human will change when, for example, the person's vantage point changes, the objects within a scene change positions or orientation, or the lighting changes (casting different shadows). Furthermore, the observer himself may introduce uncertainty into the predictions (the observer may be pre-occupied, or otherwise disposed to a particular attention pattern). Any variability in the image projected from a scene, or variability across observers, or even small changes to the scene itself, can significantly change the predictions made by these models. This can be problematic when using visual attention models in applied settings.